Introduction to Data Mining, 2nd edition

Published by Pearson (January 4, 2018) © 2019

  • Pang-Ning Tan Michigan State University
  • Michael Steinbach University of Minnesota
  • Vipin Kumar University of Minnesota


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For courses in data mining and database systems.

Introducing the fundamental concepts and algorithms of data mining

Introduction to Data Mining offers a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers and professionals. Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems.

This 2nd Edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.

Hallmark features of this title

  • Support materials, such as PowerPoint lecture slides, group projects, algorithms and data sets, are available online to promote continued learning and practice.
  • Online tutorials give step-by-step instructions for selected data mining techniques using actual data sets and data analysis software to connect the subject matter to real-life examples.

New and updated features of this title

  • NEW: The text now provides more in-depth coverage of big data as a result of developments in the industry. It includes chapter edits in response to these advances.
  • NEW: An additional final chapter discusses statistical concepts in the context of data mining techniques, something not found in other textbooks.
  • NEW: New and updated approaches to data mining are now covered, specifically in the anomaly detection section.
  • UPDATED: The classification chapters have been significantly changed to reflect the latest information in the industry, including in a new section on deep learning and updates to the advanced classification chapter.
  • UPDATED: Discussion sections have been expanded and clarified, and now include new topics.
  1. Introduction
  2. Data
  3. Classification: Basic Concepts and Techniques
  4. Classification: Alternative Techniques
  5. Association Analysis: Basic Concepts and Algorithms
  6. Association Analysis: Advanced Concepts
  7. Cluster Analysis: Basic Concepts and Algorithms
  8. Cluster Analysis: Additional Issues and Algorithms
  9. Anomaly Detection
  10. Avoiding False Discoveries

About our authors

Dr Pang-Ning Tan is a Professor in the Department of Computer Science and Engineering at Michigan State University. He received his MS degree in Physics and PhD degree in Computer Science from University of Minnesota. His research interests focus on the development of novel data mining algorithms for a broad range of applications, including climate and ecological sciences, cybersecurity and network analysis. He has published more than 130 technical papers in the area of data mining, including top conferences and journals such as KDD, ICDM, SDM, CIKM and TKDE.

Dr. Michael Steinbach is a research scientist in the Department of Computer Science and Engineering at the University of Minnesota, from which he earned a BS degree in Mathematics, an MS degree in Statistics, and MS and PhD degrees in Computer Science. His research interests are in the areas of data mining, machine learning and statistical learning and its applications to fields such as climate, biology and medicine. This research has resulted in more than 100 papers published in the proceedings of major data mining conferences or computer science or domain journals. Previous to his academic career, he held a variety of software engineering, analysis and design positions in industry at Silicon Biology, Racotek and NCR.

Dr. Anuj Karpatne is a Post-Doctoral Associate in the Department of Computer Science and Engineering at the University of Minnesota. He received his M.Tech in Mathematics and Computing from the Indian Institute of Technology Delhi, and a PhD in Computer Science at the University of Minnesota under the guidance of Professor Vipin Kumar. His research interests lie in the development of data mining and machine learning algorithms for solving scientific and socially relevant problems in varied disciplines such as climate science, hydrology and healthcare. His research has been published in top-tier journals and conferences such as SDM, ICDM, KDD, NIPS, TKDE and ACM Computing Surveys.

Dr. Vipin Kumar is a Regents Professor at the University of Minnesota, where he holds the William Norris Endowed Chair in the Department of Computer Science and Engineering. His research interests include data mining, high-performance computing and their applications in Climate/Ecosystems and health care. Kumar's foundational research been honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD) and the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society's highest awards in high performance computing.

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